Predicting Task-Specific Webpages for Revisiting

Arwen Twinkle Lettkeman, Simone Stumpf, Jed Irvine, Jonathan Herlocker

With the increased use of the web has come a corresponding increase in information overload that users face when trying to locate specific webpages, especially as a majority of vis-its to webpages are revisits. While automatically created browsing history lists offer a potential low-cost solution to re-locating webpages, even short browsing sessions gener-ate a glut of webpages that do not relate to the user's infor-mation need or have no revisit value. We address how we can better support web users who want to return to informa-tion on a webpage that they have previously visited by building more useful history lists. The paper reports on a combination technique that semi-automatically segments the webpage browsing history list into tasks, applies heuristics to remove webpages that carry no intrinsic revisit value, and uses a learning model, sensitive to individual users and tasks, that predicts which webpages are likely to be revisited again. We present results from an empirical evaluation that report the likely revisit need of users and that show that adequate overall prediction accuracy can be achieved. This approach can be used to increase utility of history lists by removing information overload to users when revisiting webpages.

Subjects: 6.3 User Interfaces; 12. Machine Learning and Discovery


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